{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "cb19f71d-51c8-417f-829e-3179d3319dcd",
    "_uuid": "c3b9226e142667d6b96e34daf7d6e42bea0ea1e2"
   },
   "source": [
    "# **通过Logistic Regression预测Titanic乘客是否能在事故中生还**\n",
    "<font color='red'>请搜索\"TODO\"字样, 在有\"TODO\"字样的地方添加代码完成作业</font>\n",
    "\n",
    "1. [导入工具库和数据](#t1.)\n",
    "2. [查看缺失数据](#t2.)\n",
    "    * 2.1. [年龄](#t2.1.)\n",
    "    * 2.2. [仓位](#t2.2.)\n",
    "    * 2.3. [登船地点](#t2.3.)\n",
    "    * 2.4. [对数据进行调整](#t2.4.)\n",
    "        * 2.4.1 [额外的变量](#t2.4.1.)\n",
    "3. [数据分析](#t3.)\n",
    "4. [Logistic Regression](#t4.)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "33c91cae-2ff8-45a6-b8cb-671619e9c933",
    "_uuid": "0a395fd25f20834b070ef55cb8987c8c1f9b55f9"
   },
   "source": [
    "<a id=\"t1.\"></a>\n",
    "# 1. 导入工具库和数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "de05512e-6991-44df-9599-da92a7e459ac",
    "_uuid": "d8bdd5f0320e244e4702ed8ec1c2482b022c51cd"
   },
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "\n",
    "from sklearn import preprocessing\n",
    "import matplotlib.pyplot as plt \n",
    "plt.rc(\"font\", size=14)\n",
    "import seaborn as sns\n",
    "sns.set(style=\"white\") #设置seaborn画图的背景为白色\n",
    "sns.set(style=\"whitegrid\", color_codes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "e0a17223-f682-45fc-89a5-667af9782bbe",
    "_uuid": "7964157913fbcff581fc1929eed487708e81ac9c"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pclass</th>\n",
       "      <th>survived</th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>ticket</th>\n",
       "      <th>fare</th>\n",
       "      <th>cabin</th>\n",
       "      <th>embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
       "      <td>female</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24160</td>\n",
       "      <td>211.3375</td>\n",
       "      <td>B5</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Allison, Master. Hudson Trevor</td>\n",
       "      <td>male</td>\n",
       "      <td>0.9167</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Allison, Miss. Helen Loraine</td>\n",
       "      <td>female</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
       "      <td>male</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
       "      <td>female</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pclass  survived                                             name     sex  \\\n",
       "0     1.0       1.0                    Allen, Miss. Elisabeth Walton  female   \n",
       "1     1.0       1.0                   Allison, Master. Hudson Trevor    male   \n",
       "2     1.0       0.0                     Allison, Miss. Helen Loraine  female   \n",
       "3     1.0       0.0             Allison, Mr. Hudson Joshua Creighton    male   \n",
       "4     1.0       0.0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)  female   \n",
       "\n",
       "       age  sibsp  parch  ticket      fare    cabin embarked  \n",
       "0  29.0000    0.0    0.0   24160  211.3375       B5        S  \n",
       "1   0.9167    1.0    2.0  113781  151.5500  C22 C26        S  \n",
       "2   2.0000    1.0    2.0  113781  151.5500  C22 C26        S  \n",
       "3  30.0000    1.0    2.0  113781  151.5500  C22 C26        S  \n",
       "4  25.0000    1.0    2.0  113781  151.5500  C22 C26        S  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将数据读入 DataFrame\n",
    "df = pd.read_csv(\"./titanic_data.csv\")\n",
    "\n",
    "# 预览数据\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_cell_guid": "872d0de9-a873-4b60-b1ee-d557ee39d8a1",
    "_uuid": "d38222a64d4dfd1d1ee1a7ee1f58c4aa54560de3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据集包含的数据个数 1310.\n"
     ]
    }
   ],
   "source": [
    "print('数据集包含的数据个数 {}.'.format(df.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "6578c0da-7bcf-433d-9f28-a66d8dfa6fa3",
    "_uuid": "8660e63a62c2fcdb4f7633380166438caf5edae9"
   },
   "source": [
    "<a id=\"t2.\"></a>\n",
    "# 2. 查看缺失数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_cell_guid": "29dddd33-d995-4b0f-92ea-a361b368cc42",
    "_uuid": "d4fe22ead7e187724ca6f3ba7ba0e6412ae0e874"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pclass         1\n",
       "survived       1\n",
       "name           1\n",
       "sex            1\n",
       "age          264\n",
       "sibsp          1\n",
       "parch          1\n",
       "ticket         1\n",
       "fare           2\n",
       "cabin       1015\n",
       "embarked       3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看数据集中各个特征缺失的情况\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "7776faeb-6a8f-4460-a367-4b087d2cc089",
    "_uuid": "696b428bd3ca49421f650665267ce7ca1b358814"
   },
   "source": [
    "<a id=\"t2.1.\"></a>\n",
    "## 2.1.    年龄"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_cell_guid": "d4ee6559-6d0c-409d-9dca-1d105a4ccd8a",
    "_uuid": "129cf984d05d9ce97c54548145e65f9e4b9b0c37"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\"age\" 缺失的百分比  20.15%\n"
     ]
    }
   ],
   "source": [
    "# \"age\" 缺失的百分比 \n",
    "print('\"age\" 缺失的百分比  %.2f%%' %((df['age'].isnull().sum()/df.shape[0])*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "951f7bb8-779c-4eac-85a2-3fdcfdcd293e",
    "_uuid": "c8fff460fb532a063f6944450809014ca831ca52"
   },
   "source": [
    "约 20% 的乘客的年龄缺失了. 看一看年龄的分别情况."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_cell_guid": "6d65fcfa-52bf-45ab-b959-64a32c1c1976",
    "_uuid": "c6fd60f15d5e803d4dffc89e782c6fbc72445a83"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1bc83a90>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df[\"age\"].hist(bins=15, color='teal', alpha=0.6)\n",
    "ax.set(xlabel='age')\n",
    "plt.xlim(-10,85)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "e62d6951-d968-43ba-aabf-add90524d042",
    "_uuid": "24c201948b9c8c8076ab01271a4790d9db9096b5"
   },
   "source": [
    "由于“年龄”的偏度不为0, 使用均值替代缺失值不是最佳选择, 这里可以选择使用中间值替代缺失值\n",
    "\n",
    "\n",
    "<font color=red> 注: 在概率论和统计学中，偏度衡量实数随机变量概率分布的不对称性。偏度的值可以为正，可以为负或者甚至是无法定义。在数量上，偏度为负（负偏态）就意味着在概率密度函数左侧的尾部比右侧的长，绝大多数的值（不一定包括中位数在内）位于平均值的右侧。偏度为正（正偏态）就意味着在概率密度函数右侧的尾部比左侧的长，绝大多数的值（不一定包括中位数）位于平均值的左侧。偏度为零就表示数值相对均匀地分布在平均值的两侧，但不一定意味着其为对称分布。</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_cell_guid": "1d70c27b-1e4d-4d5e-8a39-c134389d436c",
    "_uuid": "4f13840d4f9bf1b4331523c99274aa0627485e6c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean of \"Age\" is 29.88\n",
      "The median of \"Age\" is 28.00\n"
     ]
    }
   ],
   "source": [
    "# 年龄的均值\n",
    "print('The mean of \"Age\" is %.2f' %(df[\"age\"].mean(skipna=True)))\n",
    "# 年龄的中间值\n",
    "print('The median of \"Age\" is %.2f' %(df[\"age\"].median(skipna=True)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "dea7b01c-c8c1-401f-a336-36ee73de2222",
    "_uuid": "e1a08114e302ddc90266e5f065b3f0b5a200bc89"
   },
   "source": [
    "<a id=\"t2.2.\"></a>\n",
    "## 2.2. 仓位"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_cell_guid": "1a1ad808-0a63-43ac-b757-71195880ed4f",
    "_uuid": "1acbce9c6bc5d586dda3e47b7506067a85524e66"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\"Cabin\" 缺失的百分比 77.48%\n"
     ]
    }
   ],
   "source": [
    "# 仓位缺失的百分比\n",
    "print('\"Cabin\" 缺失的百分比 %.2f%%' %((df['cabin'].isnull().sum()/df.shape[0])*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "eda8c434-63ff-4875-8566-2e194c0d3f66",
    "_uuid": "b6e037c7ac5ec476516031a06b042d8b9999ba44"
   },
   "source": [
    "约 77% 的乘客的仓位都是缺失的, 最佳的选择是不使用这个特征的值."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "0e696cff-ca80-4cb5-862c-ee80f4b1ab1f",
    "_uuid": "d575319b1f528c7a153d8ab680282048cb163b14"
   },
   "source": [
    "<a id=\"t2.3.\"></a>\n",
    "## 2.3. 登船地点"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_cell_guid": "f21c2b55-2126-439d-8b1d-e96dafc97d81",
    "_uuid": "92ab9e62fb62f2a0fb9972baf6ada444187540e6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\"Embarked\" 缺失的百分比 0.23%\n"
     ]
    }
   ],
   "source": [
    "# 登船地点的缺失率\n",
    "print('\"Embarked\" 缺失的百分比 %.2f%%' %((df['embarked'].isnull().sum()/df.shape[0])*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "d03a4187-c527-4f71-8260-0495f4523e9e",
    "_uuid": "dc97b80524057522f024d0ae6f1abe77cb994903"
   },
   "source": [
    "只有 0.23% 的乘客的登船地点数据缺失, 可以使用众数替代缺失的值."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_cell_guid": "22924bc4-5dfa-4df7-b0d0-de3ede9c58b7",
    "_uuid": "f2a915f45264f8a580de6cc382d96b370eb75730"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "按照登船地点分组 (C = Cherbourg, Q = Queenstown, S = Southampton):\n",
      "S    914\n",
      "C    270\n",
      "Q    123\n",
      "Name: embarked, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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Dx9AYUBHx0og4PTMfzsyPZeafAG8F3tjv2rRL3wKe2x7qeFxEvLed29CeZyEeWgQMjUH2S+B9EfF0ePxY69E0h6o02L4OPApcHBFzASLiFJo5DeekBtsdwLERMR8gIg5p/+39Ac2XgVnPOY0BFhGvA95OMykHcDNwYWbWb1OovoiIg4ErgOcBm4Af0Zxya+gPsDYgbgbuAy6gmQR/JfAsYGVmru9jeQPB0JCkHhGxH801Ni8BNtJcYzOH5hT4VZnZ3f1w9wCGhiTtQnvtzUsy86Z+19JvhoYkqcyJcElSmaEhSSozNCRJZYaGNAURcUlEfHiafbwwIsam2ceHI+KS6fQhTYahIUkq834amrUi4lRgFbCA5gr8C4FTaC7kehbNSrW3A1+iWWH4GcBFmfnJtoujIuIW4CCaK/Xfkpk/i4iXAX/R9vtk4OOZ+e6IeCHwd8AvgP2Bi3pqORH4F+A1mfmNiWrLzH9r79PxT8BSYAPNvTpu7eDlkSbkSEOzUkQcCbyX5tz7Y4FzgE/RfJifCCwHjgJOprkHxvOBPwMu7elmMfAq4Dk0F3+taq8ovgB4XWY+j2b5iXe2V4gDDNMEw1JgvK3lRcAa4NQ2MCasrb3Pw6XAr4BnAyuAmOnXRtoZRxqarU4GFgHrIx7/3N1KEwRfzsyfAkTEI8AX2+e/RzOq2O5TmfnDtt3HgA9m5jvaUcLLImIlTfDMoQkjgO9n5kM9fRwO3ARclZl3F2pbBrwtM7cBP4yIG6f3MkiTY2hotpoLrO+950VEPI3mW/2BO7TdcZns7bb0/DwH2NSOBu4EbqRZuHA18Ef8eqn0n+/Qx2aa5So+ExH/mpnf3EltjwDb+M1l1zfv4u+UZpSHpzRbfQV4cUQ8GyAiXgLcDew7iT5eHhEHtivZngOsA44EfptmjaLP0dwjfIgnXlb7B5n5DZr5lLXtukc7q+2LwBsjYp+IOBA4bTJ/tDRdhoZmpcz8Ls0H/bURcRfNPaFfTjNJXXUPzaGlfwf+F3g/zYf7TcB9EfHtts97aA4t7ayej9OsrPqhJ6otM38BXEIz8rkP+Fz7u6XdxrWnJElljjQkSWWGhiSpzNCQJJUZGpKkMkNDklRmaEiSygwNSVKZoSFJKvt/YkUsdwr6XlMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109e17748>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('按照登船地点分组 (C = Cherbourg, Q = Queenstown, S = Southampton):')\n",
    "print(df['embarked'].value_counts())\n",
    "sns.countplot(x='embarked', data=df, palette='Set2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_cell_guid": "def67427-3257-4dce-872e-7f5b4202d18a",
    "_uuid": "c57a9f8a54efa382bc94b695c9664330d01709ea"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "乘客登船地点的众数为 S.\n"
     ]
    }
   ],
   "source": [
    "print('乘客登船地点的众数为 %s.' %df['embarked'].value_counts().idxmax())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "c4c55f99-ce99-44f9-b7a8-d4d623ae9295",
    "_uuid": "19cfaae8c484dcb1d00f69b2771e86dc249e9793"
   },
   "source": [
    "由于大多数人是在南安普顿(Southhampton)登船, 可以使用“S”替代缺失的数据值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "684c308f-25ae-4039-9332-ddb58953a054",
    "_uuid": "3609e785d210d5a8110f7ce550e61007d066449b"
   },
   "source": [
    "<a id=\"t2.4.\"></a>\n",
    "## 2.4. 根据缺失数据情况调整数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "b3025cdc-fe9f-43b6-bda1-e45c1f25e77c",
    "_uuid": "06d2762ccec3f11564870fe941fc9ac45d71662f"
   },
   "source": [
    "基于以上分析, 我们进行如下调整:\n",
    "* 如果一条数据的 \"Age\" 缺失, 使用年龄的中位数 28 替代.\n",
    "* 如果一条数据的 \"Embarked\" 缺失, 使用登船地点的众数 “S” 替代.\n",
    "* 由于太多乘客的 “Cabin” 数据缺失, 从所有数据中丢弃这个特征的值."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "_cell_guid": "bc0d7121-1008-4890-9043-07eba1524e15",
    "_uuid": "feeed4b6775f88edf5de12b0ee6ee73c16eba61d"
   },
   "outputs": [],
   "source": [
    "data = df.copy()\n",
    "data[\"age\"].fillna(df[\"age\"].median(skipna=True), inplace=True)\n",
    "data[\"embarked\"].fillna(df['embarked'].value_counts().idxmax(), inplace=True)\n",
    "data.drop('cabin', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "_cell_guid": "0cfe1c08-71a6-493e-803d-db255af01697",
    "_uuid": "d6be29651bb903964e02d3a7bcc7033513eb76c9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pclass      1\n",
       "survived    1\n",
       "name        1\n",
       "sex         1\n",
       "age         0\n",
       "sibsp       1\n",
       "parch       1\n",
       "ticket      1\n",
       "fare        2\n",
       "embarked    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 确认数据是否还包含缺失数据\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 按照以上处理的方式, 处理仍然存在缺失数据的情况\n",
    "TODO:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_cell_guid": "10dcfe1b-34f1-4bd8-b937-5ae8daf4a378",
    "_uuid": "3ee37b1151416aeeec8ebd7b94bb0184aabc57cd"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pclass</th>\n",
       "      <th>survived</th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>ticket</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
       "      <td>female</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24160</td>\n",
       "      <td>211.3375</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Allison, Master. Hudson Trevor</td>\n",
       "      <td>male</td>\n",
       "      <td>0.9167</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Allison, Miss. Helen Loraine</td>\n",
       "      <td>female</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
       "      <td>male</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
       "      <td>female</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pclass  survived                                             name     sex  \\\n",
       "0     1.0       1.0                    Allen, Miss. Elisabeth Walton  female   \n",
       "1     1.0       1.0                   Allison, Master. Hudson Trevor    male   \n",
       "2     1.0       0.0                     Allison, Miss. Helen Loraine  female   \n",
       "3     1.0       0.0             Allison, Mr. Hudson Joshua Creighton    male   \n",
       "4     1.0       0.0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)  female   \n",
       "\n",
       "       age  sibsp  parch  ticket      fare embarked  \n",
       "0  29.0000    0.0    0.0   24160  211.3375        S  \n",
       "1   0.9167    1.0    2.0  113781  151.5500        S  \n",
       "2   2.0000    1.0    2.0  113781  151.5500        S  \n",
       "3  30.0000    1.0    2.0  113781  151.5500        S  \n",
       "4  25.0000    1.0    2.0  113781  151.5500        S  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 预览调整过的数据\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 查看年龄在调整前后的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "_cell_guid": "dda26046-b93b-49ee-a52e-35355ecb425c",
    "_uuid": "293aec20df86ef529d10ae1f051dfe921ba07b88"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1bc83668>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "ax = df[\"age\"].hist(bins=15, normed=True, stacked=True, color='teal', alpha=0.6)\n",
    "df[\"age\"].plot(kind='density', color='teal')\n",
    "ax = data[\"age\"].hist(bins=15, normed=True, stacked=True, color='orange', alpha=0.5)\n",
    "data[\"age\"].plot(kind='density', color='orange')\n",
    "ax.legend(['Raw Age', 'Adjusted Age'])\n",
    "ax.set(xlabel='Age')\n",
    "plt.xlim(-10,85)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "6925fcc2-977b-4369-85e1-77a9210326a7",
    "_uuid": "d8280757e6bc627821fb0540c87ccd6ca110f1e0"
   },
   "source": [
    "<a id=\"t2.4.1.\"></a>\n",
    "## 2.4.1. 其它特征的处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "5cf98f33-fdd5-4a16-b6bf-fa36bc8b84e0",
    "_uuid": "3bfdee842f11d27ca490f466c45ef9bf3673e7ae"
   },
   "source": [
    "数据中的两个特征 “sibsp” (一同登船的兄弟姐妹或者配偶数量)与“parch”(一同登船的父母或子女数量)都是代表是否有同伴同行. 为了预防这两个特征具有多重共线性, 我们可以将这两个变量转为一个变量 “TravelAlone” (是否独自一人成行)\n",
    "\n",
    "\n",
    "<font color='red'>注: 多重共线性(multicollinearity)是指多变量线性回归中，变量之间由于存在高度相关关系而使回归估计不准确。比如虚拟变量陷阱（英语：Dummy variable trap）即有可能触发多重共线性问题。</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_cell_guid": "759c3c8e-8db6-41d9-a1a2-058a15b338a6",
    "_uuid": "d1f5815ba663f7e8cc17d7efcff73653af5b1bdb"
   },
   "outputs": [],
   "source": [
    "## 创建一个新的变量'TravelAlone'记录是否独自成行, 丢弃“sibsp” (一同登船的兄弟姐妹或者配偶数量)与“parch”(一同登船的父母或子女数量)\n",
    "data['TravelAlone']=np.where((data[\"sibsp\"]+data[\"parch\"])>0, 0, 1)\n",
    "data.drop('sibsp', axis=1, inplace=True)\n",
    "data.drop('parch', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "e4a22367-b719-4204-952f-d2e9a3b8075e",
    "_uuid": "ca53796bf788bd3b015f1a79a97e050bafa2c770"
   },
   "source": [
    "对类别变量(categorical variables)使用独热编码(One-Hot Encoding), 将字符串类别转换为数值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "_cell_guid": "f95361e8-2533-4731-a7ab-a99cf686ed50",
    "_uuid": "4494fcbf9faa90151e20042f74d73395fac3cc8e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pclass</th>\n",
       "      <th>survived</th>\n",
       "      <th>age</th>\n",
       "      <th>fare</th>\n",
       "      <th>TravelAlone</th>\n",
       "      <th>embarked_C</th>\n",
       "      <th>embarked_Q</th>\n",
       "      <th>embarked_S</th>\n",
       "      <th>sex_female</th>\n",
       "      <th>sex_male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>211.3375</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.9167</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pclass  survived      age      fare  TravelAlone  embarked_C  embarked_Q  \\\n",
       "0     1.0       1.0  29.0000  211.3375            1           0           0   \n",
       "1     1.0       1.0   0.9167  151.5500            0           0           0   \n",
       "2     1.0       0.0   2.0000  151.5500            0           0           0   \n",
       "3     1.0       0.0  30.0000  151.5500            0           0           0   \n",
       "4     1.0       0.0  25.0000  151.5500            0           0           0   \n",
       "\n",
       "   embarked_S  sex_female  sex_male  \n",
       "0           1           1         0  \n",
       "1           1           0         1  \n",
       "2           1           1         0  \n",
       "3           1           0         1  \n",
       "4           1           1         0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对 Embarked\",\"Sex\"进行独热编码, 丢弃 'name', 'ticket'\n",
    "final =pd.get_dummies(data, columns=[\"embarked\",\"sex\"])\n",
    "final.drop('name', axis=1, inplace=True)\n",
    "final.drop('ticket', axis=1, inplace=True)\n",
    "\n",
    "final.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "1430d510-1c8d-4544-8009-3911fff7afbb",
    "_uuid": "4e26c19bf719b7086addc0e1981c00836a19f189"
   },
   "source": [
    "<a id=\"t3.\"></a>\n",
    "# 3. 数据分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "2655428b-d69d-4c0f-85ff-e31ada8e37b9",
    "_uuid": "32e9c04a3281fb1aa8c77e1406c56cd820459202"
   },
   "source": [
    "<a id=\"t3.1.\"></a>\n",
    "## 3.1. 年龄"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "_cell_guid": "9f9ca9e5-50a0-4487-ba53-815dda90af1c",
    "_uuid": "790e8d7ca89d19e276b3398e299c42893a796b79"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yuanyuan/anaconda3/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1c4058d0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "ax = sns.kdeplot(final[\"age\"][final.survived == 1], color=\"darkturquoise\", shade=True)\n",
    "sns.kdeplot(final[\"age\"][final.survived == 0], color=\"lightcoral\", shade=True)\n",
    "plt.legend(['Survived', 'Died'])\n",
    "plt.title('Density Plot of Age for Surviving Population and Deceased Population')\n",
    "ax.set(xlabel='Age')\n",
    "plt.xlim(-10,85)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "8e304d72-27f3-41cf-863f-63872f4c37df",
    "_uuid": "6c5625b454f5e01dd6b6d843d801851c14c64d1e"
   },
   "source": [
    "### 生还与遇难群体的分布相似, 唯一大的区别是生还群体中用一部分低年龄的乘客. 说明当时的人预先保留了孩子的生还机会.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "a643b196-91c6-4b12-9463-0f984fbfc91a",
    "_uuid": "337b3ced0c6423cf1d126f23a7e60c0181af6a47"
   },
   "source": [
    "<a id=\"t3.2.\"></a>\n",
    "## 3.2. 票价"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "_cell_guid": "9f31ffe1-7cd8-4169-b193-ed44e56d0bd4",
    "_uuid": "4a1c521f08460f6983eca0c4e01294fb7c86e4f9"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yuanyuan/anaconda3/lib/python3.6/site-packages/statsmodels/nonparametric/kde.py:448: RuntimeWarning: invalid value encountered in greater\n",
      "  X = X[np.logical_and(X > clip[0], X < clip[1])] # won't work for two columns.\n",
      "/Users/yuanyuan/anaconda3/lib/python3.6/site-packages/statsmodels/nonparametric/kde.py:448: RuntimeWarning: invalid value encountered in less\n",
      "  X = X[np.logical_and(X > clip[0], X < clip[1])] # won't work for two columns.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1c241f98>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "ax = sns.kdeplot(final[\"fare\"][final.survived == 1], color=\"darkturquoise\", shade=True)\n",
    "sns.kdeplot(final[\"fare\"][final.survived == 0], color=\"lightcoral\", shade=True)\n",
    "plt.legend(['Survived', 'Died'])\n",
    "plt.title('Density Plot of Fare for Surviving Population and Deceased Population')\n",
    "ax.set(xlabel='Fare')\n",
    "plt.xlim(-20,200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "346b7322-a3e4-48df-bbb1-4d8ec7716f3f",
    "_uuid": "2717310b6c443d675c7342be0c2c18b265723273"
   },
   "source": [
    "生还与遇难群体的票价分布差异比较大, 说明这个特征对预测乘客是否生还非常重要. 票价和仓位相关, 也许是仓位影响了逃生的效果, 我们接下来看仓位的分析."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "cf585311-4029-4be4-8af2-3eea8258801a",
    "_uuid": "4524affda51265ea23fa923e2ea7f93d7bb91875"
   },
   "source": [
    "<a id=\"t3.3.\"></a>\n",
    "## 3.3. 仓位"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "_cell_guid": "676548e8-6dd4-4180-800c-7b164acb3877",
    "_uuid": "08fd677214959e0b938a0f8a94b63ab548673ea5"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1c3e4908>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot('pclass', 'survived', data=df, color=\"darkturquoise\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "193233f8-b220-4cae-aa0f-f822316d5623",
    "_uuid": "8ddb19191253a6e09dfcb0beff2b3690f1052d52"
   },
   "source": [
    "如我们所料, 一等舱的乘客生还几率最高."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "c59f8e8f-e8c2-40fb-b9c8-12dddd6d318f",
    "_uuid": "2fc06b75321946b721852f78431435f9ba5fef39"
   },
   "source": [
    "<a id=\"t3.4.\"></a>\n",
    "## 3.4. 登船地点"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "_cell_guid": "6e5bec50-2f5e-433e-9130-c56956fddad3",
    "_uuid": "a9f0598701c7c5224eaa73dafa869af73beffe18"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1cd4fb00>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot('embarked', 'survived', data=df, color=\"teal\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "88d78820-35a5-48fd-a234-9f3ca3fca779",
    "_uuid": "2f6a0329cf0c7b771a707ec790efc065924e1ee2"
   },
   "source": [
    "从法国 Cherbourge 登录的乘客生还率最高"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "9e6dc87e-ba59-4004-8145-79709328fe27",
    "_uuid": "92bacce85a7dec5509217b9570bc2a2fea6a8452"
   },
   "source": [
    "<a id=\"t3.5.\"></a>\n",
    "## 3.5. 是否独自成行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "_cell_guid": "67017a88-93d4-412b-9adf-8b4d1d9b9db0",
    "_uuid": "e0c3dc16292ef0bcabf0fc680d821ef654084ab4"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1cd333c8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot('TravelAlone', 'survived', data=final, color=\"mediumturquoise\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "e9e68cef-5e74-46aa-8343-39afbbf00efe",
    "_uuid": "f160bd7399e024ae669d55f09caf6e7902768851"
   },
   "source": [
    "独自成行的乘客生还率比较低. 当时的年代, 大多数独自成行的乘客为男性居多."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "201b4c9d-b9f0-4ae9-8580-0b4e24ee62be",
    "_uuid": "693c25c25f3590f0b027725471ddd74d56f154af"
   },
   "source": [
    "<a id=\"t3.6.\"></a>\n",
    "## 3.6. 性别"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "_cell_guid": "7b416e59-8616-4a44-93e1-a8005eff78a9",
    "_uuid": "354794315925dff1e96229cc737eaf299aaea17a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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reqrZel8UC4rMnIyIDcBOqq/HfjIzd0XENuAa4EXAWcCCiJi+yX13ZnploXnv+KHj+PCju2e7jFn36IHmkz/9/wFXnbJiVs5bdBxFZo4Cox3r1rRe3o0D/iRp4PmLWpJUy6CQJNUyKCRJtQwKSQPr+JNOfMpPzQ6DQtLAistezfPPfBlx2atnu5R5zedRSBpYpzaWcWpj2ZEbqiivKCRJtQwKSVItg0KSVMugkCTVMigkSbUMCklSLYNCklTLoJAk1TIoJEm1DApJUi2DQpJUy6CQJNUqOilgRKwDNlI9M3tTZm4+TLubgJ2ZuaVkPZKk3hW7ooiIpcD1wHnAcmB9RCzraLMkIr4AXFqqDknSzJTseloN7MjMvZm5D9gKXNLR5o3AXwKfLViHJGkGSnY9LQGm2pangFXtDTLzDwAi4rx+TjAxMdF3cQCNRmNG+2vuGh8fn9Xz+97U4czGe7NkUAwdYt2Bo3mCkZERhoeHj+YhJcBf1BpcM3lvNpvNvj5gl+x6mgROa1teDOwpeD5JUgElryi2A9dGxCJgH7AWWF/wfJKkAopdUWTmJLAB2AnsBkYzc1dEbIuIlaXOK0k6uoqOo8jMUWC0Y92aQ7S7vGQdkqT+OTJbklTLoJAk1TIoJEm1DApJUi2DQpJUy6CQJNUyKCRJtQwKSVItg0KSVMugkCTVMigkSbUMCklSLYNCklTLoJAk1TIoJEm1DApJUi2DQpJUy6CQJNUq+ijUiFgHbAROADZl5uaO7SuATwDPAf4WeGtmPlGyJklSb4pdUUTEUuB64DxgObA+IpZ1NPsM8M7MPAMYAt5cqh5JUn9KXlGsBnZk5l6AiNgKXAJc11p+CXBSZo612m8B3gd8rItjLwB47LHHZlzk8OMHZ3wMzS3NZnO2SwB8b+rpZvrebPuduaCX/UoGxRJgqm15Clh1hO0v7PLYiwG+8Y1vzKQ+6ChIAphgYrZLAHxv6umO4ntzMfCtbhuXDIqhQ6w70MP2OncB51OFy497rEuS5qsFVCFxVy87lQyKSapf5tMWA3s6tp9Ws/2wGo1GE7h9pgVK0jzU9ZXEtJJfj90OXBgRiyJiIbAWuGV6Y2Z+G/hRRPzL1qrfAP6qYD2SpD4UC4rMnAQ2ADuB3cBoZu6KiG0RsbLV7I3Apoj4e+Bk4L+UqkeS1J+hgwf9ZoUk6fAcmS1JqmVQSJJqGRSSpFoGhSSplkExR0TEjRHxjYh4Q4Fjb4mIy4/2caV+RMRDEfHS2a5jPik6e6yOqcuBEzNz5hNgSVIbg2IOiIjPU02Jsisi/gi4iupqcRz4rcz8UUQ8DHyBn0x98sfAlVTza12emV+JiFdRzfi7EHgu8O7M/B8d5/qNQx3/GPwxNYdExAVU46yGgJcBW4EfAK9rrVsDXAr8W6oxVgeAX8vMv287xgLgD4ALqKam2JKZm47ZH2IesetpDsjMX229fCPVVO3/IjNXAN8F3tXadirwxcz8+dbyxZl5PnAt1S9+gHcCv5mZZwNvAq5pP09EnFlzfKlXrwSuAM4E3gZ8LzNXAvcCl1GFxgWZOQLcDLy9Y/83A7Ter6uA10bE+eio84pibvnXwM8BYxEB1QOjvtq2fXqKlG/zk7myvk119QDw68C/iYhLgXOBZ/V4fKkXE5n5HYCI+Efgb1rrp9+T64DLIuIM4JeoZnhotxpYERG/0Fp+FvBy4LbShc83BsXcsgD4bGZeCRARz6Lt77jj/sWhniR4G9WUK7dS/aMd7eX4Uo8676e1vydfBNwJfJTqA87DwFkd7RdQdY/+BUBE/DSwr0yp85tdT3PLrcDFEfGCiBiiegjUVfW7VCLiecAZwDWZuQ24iKc/3KTv40s9Ogf4Zuuew98Bv8zT3487gDdHxE+1PrTcTtWdpaPMoJhDMvMeqqcE7gC+TvX3+4Eu990LfBL4ekR8DXgBsDAiTj4ax5d69CXguIi4HxgDHgJO72jzceAB4GvA3cCnMvPWY1jjvOGkgJKkWl5RSJJqGRSSpFoGhSSplkEhSaplUEiSahkUkqRaBoUkqZbTL0g9aI0A/hTVnFcHqGbQfQvwGmAj1fxX+4F3ZeadEfEp4OTMfH1rUsWdVBPd3T8rfwCpD15RSL25GPhnrdlzz2mtexnwfmBNZp4FrAf+ojWq/R3A8oj4d8B/B64yJPRM48hsqQcRcTrwt8C3gC9TTX/9KuA64P+2NV1EFRz3RMRZVPMV/VlmvukYlyzNmFcUUg8y80HgZ4HfB54NbAeeD/xNZq6Y/o9qmvaJ1m4BfB84KyJOmIWypRkxKKQeRMTbqO5RfCkz3wP8NfAK4KKI+PlWmzVUD985sfVs548Avwj8b+A/z0bd0kzY9ST1oHXf4UaqcNgH/APV0wBX85NHez5BNf36nVTP+PhcZn4oIp4L3Ae8JTP/1yyUL/XFoJAk1bLrSZJUy6CQJNUyKCRJtQwKSVItg0KSVMugkCTVMigkSbUMCklSrf8P8wehoFB/KPsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1c1bd5c0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot('sex', 'survived', data=df, color=\"aquamarine\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "490ed298-f0e4-466b-acc8-81280315e6a2",
    "_uuid": "80c02b9fe2151c443f189cbe44c9cacf7e5c44a4"
   },
   "source": [
    "很明显, 女性的生还率比较高"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "c833cbf5-74db-44ff-90fa-b600ff0a09d7",
    "_uuid": "39dbc095f99dcec6d25a7a4561e81bb641078622"
   },
   "source": [
    "<a id=\"t4.\"></a>\n",
    "# 4. 使用Logistic Regression做预测"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "b894002e-07cf-4d02-b708-a2ac387eed54",
    "_uuid": "e35125f8aa230d4875541aa4f6b5964d2f14a6a3"
   },
   "source": [
    "\n",
    "### 将数据集分为训练和测试数据集用于检测模型效果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "84233f59-f3c7-4ea0-884d-96f8ad4d5b10",
    "_uuid": "46336228eeb864bc82e6739768122579d1c9634c"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'final' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-fd5d48f20db6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;31m# 创建 X (特征) 和 y (类别标签)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfinal\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcols\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfinal\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'survived'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'final' is not defined"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# 使用如下特征做预测\n",
    "cols = [\"age\",\"fare\",\"TravelAlone\",\"pclass\",\"embarked_C\",\"embarked_S\",\"sex_male\"] \n",
    "\n",
    "# 创建 X (特征) 和 y (类别标签)\n",
    "X = final[cols]\n",
    "y = final['survived']\n",
    "\n",
    "# 将 X 和 y 分为两个部分\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=2)\n",
    "\n",
    "# 检测 logistic regression 模型的性能\n",
    "# TODO 添加代码:\n",
    "# 1.训练模型,  \n",
    "# 2.根据模型, 以 X_test 为输入, 生成变量 y_pred\n",
    "print('Train/Test split results:')\n",
    "print(\"准确率为 %2.3f\" % accuracy_score(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
